The human brain is an exceedingly complex processing system, which integrates continual streams of incoming sensory input data with stored memories, uses the input data and memories in complex decision processes at both conscious and unconscious levels and, on the basis of these processes, generates observable behaviors by activation of its motor or movement control pathways and the muscles which these innervate. The neurons of the nervous system propagate input data by generating characteristic electrical pulses called action potentials (APs), or neural spikes, that can travel along nerve fibers in the form of neural signals. A single neuron or a group of neurons represent and transmit information by firing sequences of APs in various temporal patterns. Information is carried in the AP arrival times and spike counts.
In certain cases of traumatic injury or neurological disease, the brain can be partially isolated from the periphery. Input data from certain senses are thus lost, at least for a portion of the body, as are many voluntary movements. Spinal cord injury is a well-known example of traumatic injury. With spinal cord injury, the pathways that link higher motor centers in the brain with the spinal cord and that are used for control of voluntary movements can be functionally transected at the site of the injury. As a result, the patient is paralyzed, and can no longer voluntarily activate muscles that are innervated by regions of the spinal cord below the level of the injury. Despite the injury to their long fibers, however, many of the cells in these higher brain regions that control voluntary movement will survive and can still be activated voluntarily to generate electric signals for controlling voluntary movement. By recording the electrical activities produced from these cells with implantable neural sensors (e.g., a microwire electrode array, a microwire, a magnetic field detector, chemical sensor, or other neural sensor), a decoding algorithm can be applied to interpret and reproduce the user's intended movements for the control of external prostheses, such as an assist robot or an artificial limb, or functional electrical stimulation of paralyzed muscles. Additionally, these generated signals can be used for control of computer operations such as the movement of a cursor on a computer display. Current equipment allows extracellular records of hundreds of cortical neurons in primate subjects to be acquired onto a plurality of neural channels and processed to make predictions of hand position during a target tracking task. These predicted motor outputs can be used to control a multi-jointed robotic limb.
Current devices for obtaining neural signals employ hundreds of neural sensors and neural signals to obtain predictions of the user's intended movements. Generally, it has been assumed to require as many neural signals as can be recorded that are distributed across different areas of the frontal and parietal cortices in order to obtain the best predictions. However, even using hundreds of neural signals that are currently recorded places a significant burden on neural signal acquisition and processing equipment. It is possible that different groups of neural signals carry redundant information. If this is the case, a subset of the neural signal population may provide as much information as the whole population for prediction purposes.
Accordingly, there exists a need for improved and more efficient methods, systems, and computer program products for neural channel selection in a multi-channel system.